1. Field of the Invention
The present invention relates to the measurement and control of manufacturing processes that produce web or sheet based products, and more specifically, to use of a neural network with spatially dependent data in the control of such processes.
2. Description of the Related Art
The quality of a manufactured product can often be more financially critical than the quantity that is produced. There are many standards worldwide that provide guidelines for quality assurance between suppliers and customers. Maintaining standards of quality for a product may require consideration of the specific properties of the product, as well as the product's final use. The quality of a product is the result of the physical integration of all the raw materials, equipment, and process and operator manipulations occurring during its manufacture.
Process control can be generalized as the collection of methods used to produce the best possible product properties and process economies during the manufacturing process. Many manufacturing processes fall into one of two categories based on the spatial or dimensional dependence of product properties—longitudinal or bulk manufacturing; and web or sheet based manufacturing. Longitudinal or bulk products can be considered dimensionally homogenous and can be measured or characterized with bulk properties. Examples include plastic dowels, polymer threads, fluids, and so forth. Web-based products can be measured or characterized with spatially dependant properties. Examples include rolls and sheets of plastic, paper, or other fibers, minerals and wood products, and even some food products. Note that as used herein, the term “sheet” may refer to both flat products and rolled products.
The challenges associated with web-based products require special consideration for the manufacturing process conditions and the product properties due to the dimensional nature of web-based products. Improper control of process conditions in web-based processes, in either the direction of manufacture or across the direction of manufacture, can result in products that are of little or no value to the final customer. In these situations the manufacturer will lose profit opportunity due to the need to recycle and remanufacture the product, or sell the product at a lower price. Many customers purchase web-based products for use as a raw material in their own processes, which then convert the web product into final end user consumer products. Less than first quality web-based products are not typically accepted by customers. The ability to effectively control web based processes and web-based product properties plays a significant role in determining the profitability of manufacturing operations.
Quality and Process Conditions for General Process Control
FIG. 1 illustrates key concepts of a typical manufacturing process in generalized block diagram form. As FIG. 1 shows, raw materials 102 are transformed by a process 104 under controlled process conditions into products 106 with desired properties. FIG. 1 also presents exemplary raw materials, process conditions, and product properties for typical manufacturing processes. For example, raw materials 102 may include such bulk feed materials as chemicals, fibers, minerals, energy, and parts or components, among others. Process conditions may include such operating parameters as flow, pressure, temperature, humidity, as well as speed, rate, and feed properties, among others. Example product properties related to quality may include weight, color, strength, composition, texture, and so forth.
Controlling Process Conditions
FIG. 2 shows a more detailed representation of the manufacturing process as it relates to the production of products with specific desired properties. More specifically, FIG. 2 provides a simplified overview of various aspects of a manufacturing process, where the effective operation of the process requires that the process conditions be maintained at one or more condition set points so that the product produced will have the product properties matching the desired product property targets.
As shown in FIG. 2, various raw materials 208 may be provided to a process 210 with various process conditions, including controllable process conditions, i.e., controller/actuator parameters, where the process produces a product 212 with various product properties. The process 210 may be controlled in accordance with process condition target values 202, which may be initialized with initial process condition targets, as shown, but which may be adjusted based on feedback from measured process and property data. As may be seen, product property measurement(s) 206 may be analyzed with respect to product property target value(s) 204, and an adjustment of process condition targets determined and applied in to the process condition target values 202. As also shown, measurement(s) of process conditions may be analyzed with respect to the process condition target values 202, and adjustments of controllable process conditions made to the process accordingly. Thus, the various components of the system may operate in conjunction via feedback mechanisms to control the process to produce a product with desired properties.
The automation of manufacturing process controls allows the production of products from complex manufacturing processes that cannot be controlled by manual operation. In addition to manufacturing products at higher rates that are more economically favorable, automatic process controls allow the products to achieve more desirable product properties, more consistently. These three factors: more production throughput, more desirable product properties, and a more economical operation, form the basis of process control, which can be summarized as utilizing scientific methods to gain economic leverage over the manufacturing process.
The process control tasks shown in FIG. 2 can be generalized into five steps that apply to both manufacturing processes for products that are both longitudinal or bulk and web-based. It should be noted that the general nature of these descriptions is not intended to ignore or oversimplify the efforts necessary to control the process conditions and product properties of every manufacturing process.
1) Setting of the initial process condition set points
2) Producing process condition measurements of the process conditions
3) Adjusting the controllable process states in response to process condition measurements
4) Producing product property measurements based on product properties of the manufactured product
5) Adjusting the process condition set points to in response to the product property measurements.
Steps 2 and 4 involve measurements of process conditions and measurements of product properties necessary for control and financial success of the manufacturing operation.
Thus, as FIG. 2 indicates, the manufactured product is defined by one of more product properties, where each product property is quantified by a specific measurement, and the manufacturing process is operated to produce the targeted level of each product property as determined by its specific measurement. Each specific product property, contributes to the overall value of the manufactured product. As also shown, the product property target values, as well as process condition measurements (and initial process condition targets), determine process condition target values, which in turn may be used to adjust controllable process conditions of the process. Thus, the interplay of measured and target product properties, measured and target process conditions, and adjustments made thereto, gives rise to a feedback system whereby the quality of the final product may be tuned and maintained to desirable ends.
FIG. 3 illustrates a system representative of most manufacturing processes, where the end use and desired properties of the products produced determine the specific nature of the process and controls used to adjust the process. In other words, the physical nature of the product being produced can dictate process design, raw material configuration and the controls required to achieve the final product properties.
The example of plastic dowel extrusion shown in FIG. 3 is a simplified prior art longitudinal or bulk process presented here for illustration purposes, although the general concepts described apply to more complex manufacturing processes, as well. As may be seen, raw materials 102 (such as plastic pellets, colorants, stabilizers lubricants, etc) are processed in an extruding machine 302 (that implements a process 104) under controlled process conditions (such as temperature, pressure, flow, etc.) to produce a product 106, specifically, plastic dowels 304, as shown. Examples of the controlled process conditions could include melt materials thoroughly, mix materials uniformly, heat extrusion mechanism to preset temperature, maintain pressure through out extrusion, cool to desired temperature, and so forth. The product (dowels 304) in this example would be produced to have specifically desired properties, such as, for example, color of the dowel, weight per standard length, stiffness, tensile strength, etc.
In the general case, the actual product properties of a product produced in a process are determined by the combination of all the process conditions of the process and the raw materials that are used in the process. Process conditions can include, but are not limited to, the properties of the raw materials, the process speed, the mechanical manipulation of the process equipment, and the conditions within individual operations of the process, among many others. As mentioned above, the extrusion of a plastic dowel may be referred to as a longitudinal or bulk manufacturing process due to the relative insignificance of any latitudinal process or product considerations, i.e., due to the homogenous nature of the product in any direction other then the direction of manufacturing. Further examples of longitudinal or bulk products include liquids such as chemicals or petroleum products, solid particles of various sizes from polymeric raw materials to cement, or any other product where the properties have little or no cross manufacturing direction variability, and that can be considered homogeneous when measured over small increments of manufacturing time. The desired properties of the plastic dowel can be based on time or the relative product position in the manufacturing process.
Quality and Process Conditions for Web-based Process Control
For the case of a process specifically designed to produce a web or sheet based product there are both longitudinal and latitudinal considerations related to the raw materials, the manufacturing process, and the product properties. Web-based product properties are similarly determined by the combination of all the process conditions of the process and the raw materials that are used in the process. Web-based products can require that dimensional (i.e., 2 dimensional) considerations be given to the raw materials as part of the process being controlled. The previous example of a manufacturing process to produce plastic dowels can be compared to a corresponding manufacturing process for the production of a continuous plastic sheet or web 402, as illustrated in FIG. 4.
A simplistic generalization can be made that the manufacturing processes for the production of a plastic dowel and for the production of a plastic sheet involve approximately similar process component functions affecting the raw materials with corresponding manipulations of temperature, pressure, flow, etc., over time. The resulting products (e.g., dowels 304 and sheets 402) differ with respect to their desired product properties and how the process conditions are controlled to achieve the desired properties. Note that the plastic sheet manufacturing process and its product properties differ from the plastic rod manufacturing process and its product properties due to the (two-) dimensional nature of the processes and properties. Like the extruded plastic rod, the desired properties of the plastic sheet can be measured based on its position in the manufacturing process and can be referenced by time; however, the web-based plastic sheet must also have measurements of its manufacturing process and its product properties in the latitudinal directions.
For typical web manufacturing processes producing web-based products, the latitudinal dimension for a process condition or a product property is referenced perpendicular to the direction of manufacturing. This position reference perpendicular to or across the manufacturing direction is typically referred to as the cross direction position or CD position, while the product property position referenced to the manufacturing direction is typically referred to as the manufacturing or machine direction position or MD position, each of which is illustrated in FIG. 5. Specifically, a 1D process/product (bottom) is shown to have only an MD direction 504, while the web-based product 402 is shown to have an MD direction 504, as well as a CD direction 502, which may be seen to be perpendicular to the MD direction 504, i.e., to the direction of motion or travel.
Measuring Process Conditions and Product Properties.
As described above, there are specific steps in a generalized process control strategy that require measurements of process conditions and measurement of product properties, however, there are manufacturing process measurements and product property measurements that can be difficult to obtain due to the inherent nature of the physical measurement, the location at which the desired measurement must be taken, or the time needed to procure an accurate measurement. In other words, certain process condition measurements can be difficult to reliably acquire due to location, environment, accuracy or other considerations that limit the usefulness of the process condition data in a process control system or strategy, and various product property measurements data can be difficult to acquire do to similar considerations. Product property measurements have an additional constraint on their usefulness associated with the time required to produce an accurate and reliable measure of the specific product property. It is not uncommon for property measurements of certain products to require hours, even days or weeks before an accurate product property measurement is available, e.g., product properties involving physical performance or destructive testing such as strengths, shelf life, wear, color fastness, etc.
The economic viability of a manufacturing operation can be critically dependant on the timely availability of accurate process condition measurements and product property measurements. The inability to obtain accurate and timely measurements can affect the efficiency of the manufacturing process as well as the quality of the products produced.
Web-Based Measurements
It can thus be appreciated that the dimensional nature of web-based process conditions and web-based product properties that have the additional requirement of cross manufacturing direction measurements associated with any point in time, requires unique consideration.
FIG. 6 illustrates a typical web-based product 402 and the relative dimensions related to the raw materials and the web-based manufacturing process, as well as a comparison of product property measurement considerations that can arise from the need to measure the same product property on two products made from roughly similar raw materials, but produced through different manufacturing processes, specifically longitudinal or bulk (e.g., dowel production), and web-based (sheet production).
Referring to the previous examples of the extruded plastic dowel and the extruded plastic film, a comparison of the two indicates that the web-based product may require additional measurements of the same desired property across the web-based product at a specific instant in time to characterize the desired product property, as compared to the characterization of the longitudinal or bulk product. In other words, as may be seen in FIG. 6, the dimensional nature of a web-based product generally requires more measurements to provide a similar level of process condition and product property measurement or characterization. Referring to FIG. 6, a measured product property (PP), for both the dowel product 304 and the web product 402 taken at times T1 and T2 are represented by respective labels. For the longitudinal or bulk product, there is only one measurement, per time, e.g., PP T1, which denotes Product Property (PP) at time T1. For the Web-based product, there are multiple measurements per time, PP T1CD1, which denotes Product Properties (PP) at time T1 at position CD1, PP T1CD2, and so forth, for each CD position across the product web. As also shown, such cross direction measurements are also made for these CD positions at time T2. Improper or incomplete measurement of the desired property across the product web (i.e., at the various CD positions) can result in improper or incomplete adjustment of process conditions targets and a product of lesser quality and value. As is well known in the art of web-based product manufacturing, the total width of the manufacturing process and the product can be segmented spatially into smaller individual increments of the cross manufacturing direction width to facilitate higher resolution measurement and control of the process conditions and product properties. Each of these individual spatial segments of the manufacturing process produces a corresponding spatial segmentation in the product, e.g. longitudinal bands or strips running along the web product (in the time or manufacturing direction) associated with or defined by corresponding CD positions, e.g., the respective CDn positions at any or all time or manufacturing direction points. For example, a strip of the product running along the near edge of the product may be referred to as CD zone 1, profile zone 1, data box 1, or simply CD1. Thus, all the CD1 measurements that are made in or taken near the front edge may be considered as being spatially contained in CD zone 1 or as being from that particular CD zone. It should also be noted that different measurements reported as being made at a particular CD position, e.g., CD1, may not be taken from exactly the same position, e.g., due to the spatial requirements of some sensors. In other words, a set of measurements may be made within some particular CD zone, e.g., in a cluster within that zone or strip, and may be considered to be at that location. Similarly, as described below, actual measured data may be used to synthesize additional data (e.g., via interpolation, extrapolation, etc.) associated with particular CD positions, even if the actual measured data were not taken from those exact positions.
For many web-based products, there are important product properties that relate to the final end use and quality of the product and thus require additional, or subsequent, product property measurements in order to be acceptable. For example, the printability of paper may not be known (as it is being produced on a paper making machine), until it is shipped to a printer for testing. It is also common in the case of web-based products that some important web-based product property (or properties) cannot be measured directly. For example, some important properties may relate to the rate of variance of a property, i.e., may be based on minute differences that are spatially adjacent. In other words, the product's value or quality, and thus, the product's acceptability, may relate to the magnitudes of spatially adjacent product properties as they vary across the product. As an example, a thickness variance of 1 millimeter in sheet glass that occurs over a meter may not be noticeable, while that same variance over a centimeter may produce a noticeable distortion in the glass's transmissivity, e.g., a visible “ripple”, and so may result in an unacceptable product.
Having these spatially dependent measurements across a web-based product would improve the overall control of the process conditions and/or product properties. Moreover, it may be beneficial for these measurement data to be spatially coherent, where, as used herein, the term “spatially coherent” refers to data that preserve their spatial relationships, e.g., that have associated position data whereby such spatial distribution may be preserved, or that are organized in such a way that preserves the spatial relationships or relative distribution of the data, e.g., the spatial relationships of the measurements (actual or synthesized). It should be noted that “spatially coherent” does not mean that the positions of the data are necessarily regularly spaced, or in any particular arrangement, but only that the spatial distribution of the data or spatial relationships among the data (which could be randomly distributed) are preserved, although such regular spacing is certainly not excluded.
Note that as used herein, the terms “array”, “array data”, “spatial array data” and “spatially coherent data” may be used to refer to data sets whose elements include positional information for the data contained therein, or to data arranged to preserve the positional information, not to the particular type of data structure used to store the data.
The same requirement for multiple measurements applies to web-based manufacturing process conditions. Referring to the previous examples of the extruded plastic dowel and the extruded plastic film, web-based manufacturing processes may require additional measurements of the same desired process condition across the web-based process at each specific instant in time to characterize the desired process measurement in a manner corresponding to the characterization of the longitudinal process, as illustrated in FIG. 7, where the process condition measurements are represented in FIG. 6 by respective labels, PC T1 CD1, etc., that denote Product Conditions (PC) at time T1 at position CD1, and so forth, for each time and CD position. Improper or incomplete measurement of the desired process conditions across the process can result in improper or incomplete adjustment of process conditions and thus in a product of lesser quality and value.
There are many instances in web-based manufacturing processes where critical data from multiple measurements of process conditions or product properties occur at the same instant in time, or that are reported as having occurred at the same instant in time, e.g., stored with a single time stamp or other order denotation. These multiple process condition measurements and multiple product property measurements can be contained in a data array with an information structure that can establish the positions of the individual measurements within the array structure relative to the cross manufacturing direction position (CD) of the process condition or the product property. As noted above, within a data array, the spatial or positional relationships of the individual measurements can be maintained structurally, e.g., implicitly, via the data structure that contains the data, or explicitly, i.e., via additional information included or associated with the data.
It is also common in web based process industries that process condition and product property measurements are taken with devices that require some time to acquire or process the measurements, such as, for example, traversing measurement sensors that move across a field making a series of measurements in succession, in which case, the entire measurement data array may be reported as having occurred at the same instant in time. In other words, although the series of measurements occurred over a span of time, the resulting data array may be reported and/or stored as a measurement data array with a single order or time stamp. It should be noted that instead of including a time or order stamp, the data from successive measurements may simply be maintained (e.g., stored) in such a way as to preserve their order. In other words, the temporal ordering may be implicit (e.g., organizational), rather than explicit (i.e., including time or order stamps).
Thus, the data array can also contain information or be organized in a way that establishes the measurement position of the data array relative to the manufacturing direction (MD) or to time, which may be referred to as temporally coherent or ordered. In some web-based manufacturing processes these data arrays containing process condition measurements or product property measurements can be referred to as ‘profile arrays’ or simply as ‘profiles’.
FIG. 8 illustrates an example of a product property measurement data array represented as cross manufacturing direction measurements (CD product property measurements) or a ‘product property profile’, where in this particular case, the product property is the web or sheet thickness, and where the measurements are displayed (graphically) with respect to reference CD positions 1-20. FIG. 8 also shows an example of a process condition measurement data array represented as cross manufacturing direction measurement (CD process condition measurements), in this case, a ‘process condition actuator profile’, where in this particular case, the process condition is the process gap or opening, and where the measurements are also displayed (graphically) with respect to reference CD positions 1-20. These types of profile data arrays are used in one form or another in most web-based process industries including, for example, paper, non-wovens, textiles, wood products, etc., among others.
FIG. 9 illustrates a typical technology used to measure web-based product properties in the cross direction, although it should be noted that other technologies may also be used. Note that an array of stationary measurement sensors 904 may be used to acquire CD measurements spanning the product's width at each specified instant of time, while traversing measurement sensors 902 make measurements serially, with a sensor 908 moving back and forth across the web 402 as the web travels along the manufacturing path, resulting in a “zigzag” measurement pattern.
Depending on the source of the product property measurement, directly from product property measurement device within the process or from a product property measurement device subsequent to the process, the data array can be order or time stamped or otherwise marked to indicate the measurement array data's relative position in the web-based manufacturing process, its relative position within the web-based product, or its occurrence in time.
The multiple measurement data of process condition or product properties arising from web-based products must be mathematically reduced to a single ‘average’ or otherwise representative order or time stamp value to accommodate the limitations of current neural network modeling technology and methods.
Neural Networks as Predictors of Process and Property Measurements
Current computer fundamental models, computer statistical models, and neural network models can address certain specific process condition measurement and product property measurement deficiencies related to these physical or time constraints in certain manufacturing processes. An exemplary current neural network based approach to process measurement and control is disclosed in U.S. Pat. No. 5,282,261 to Skeirik, which is incorporated by reference below.
Currently available neural network technology can provide predicted values of process condition measurement data and product property measurement data that may not be readily measured. For example, the prior art technique referenced above requires that the input data be specifically time stamped for training and that the predicted data be specifically time stamped for further use in a controller or in a control strategy. Time stamped process condition data and product property data are considered discrete data points, in that each individual measurement data point is detached or independent from any other and clearly identified by a time stamp that can establish its position relative to the manufacturing process.
FIG. 10 illustrates a simplified exemplary embodiment of a neural network. As FIG. 10 shows, input data are provided to an input layer, including a plurality of input elements or nodes, each of which may be coupled to a plurality of elements or nodes comprised in a hidden layer. Each of these hidden layer elements or nodes may in turn be coupled to each of a plurality of output elements or nodes in an output layer of the neural network. These output nodes provide respective output data, which may be used to predict and/or control a process. As is well known in the art of neural networks, adjustable weights associated with the various node couplings are modified and set in a training phase, and subsequently determine the resulting behavior of the neural network. This existing neural network technology and method is most readily applied to longitudinal manufacturing processes, as exemplified in the extruded plastic dowel example described above.
Prior art neural network applications in process control (see, e.g., U.S. Pat. No. 5,282,261) have evolved through various computer-based modeling strategies including, for example, first principles modeling, statistical and empirical modeling; and non-conventional neural networks. The current limitations for utilizing model-based neural network applications in web-based process control require statistical averaging or manipulations of the available spatial array data that significantly reduces the usefulness of those very process condition and product property spatial array measurements.
Averaging spatial array data to produce a single data point in order to accommodate modeling limitations effectively defeats the basic purpose of using computer modeling and neural network modeling to produce measurements that are difficult to obtain. This type of data handling renders the neural network technology relatively ineffective for treating web-based measurement for process conditions and/or product properties.
Currently there are considerable deficiencies in conventional approaches to obtaining desired measurements for web-based manufacturing process conditions and web-based product properties. Specifically, there are no available methods for directly utilizing spatial array based data that are typically derived from web-based manufacturing processes, nor are there well-defined web-based product properties for modeling and predicting desired measurements of process conditions and product properties. Web-based manufacturing processes and products thus present a unique challenge to existing neural network technologies due to the need to accommodate array based measurements that are referenced in both the manufacturing direction and the cross manufacturing directions, as well as in time.